NDLI: Boosting normalized sparsity regularization for blind image deconvolution

Content Provider	SpringerLink
Author	Yang, Chang Xing Shao, Wen Ze Huang, Li Li
Copyright Year	2016
Abstract	We focus on blind image deconvolution, which has attracted intensive attentions since Fergus et al.’s influential work in 2006. Among the current literature, the daring idea of imposing the normalized sparsity measure on blind image deblurring is a recent spotlight, which is, nevertheless, far from practical use in terms of estimating accuracy, efficiency, as well as robustness. To boost its performance, we propose a novel method via coupling the normalized sparsity measure with the total generalized variation, which, however, does not fit the blind deblurring problem. By use of operator splitting and alternating direction method of multipliers, a numerical scheme is derived, leading to a more accurate, efficient, and robust blind deblurring algorithm. Numerous blind deblurring results on both benchmark data and real-world blurred images demonstrate the competitive or even better performance compared against the state of the art.
Starting Page	681
Ending Page	688
Page Count	8
File Format	PDF
ISSN	18631703
Journal	Signal, Image and Video Processing
Volume Number	11
Issue Number	4
e-ISSN	18631711
Language	English
Publisher	Springer London
Publisher Date	2016-11-17
Publisher Place	London
Access Restriction	Subscribed
Subject Keyword	Blind image deblurring Total generalized variation (TGV) Normalized sparsity measure Blur kernel estimation ADMM Signal,Image and Speech Processing Image Processing and Computer Vision Computer Imaging, Vision, Pattern Recognition and Graphics Multimedia Information Systems
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Signal Processing

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